Open Source LiteLLM AI Gateway Alternative? LiteLLM vs MLflow

This article is for teams looking for a secure, enterprise-ready, open-source alternative to LiteLLM. We compare the two across governance, security, performance, tracing, and complete AI platform capabilities to help you decide which is the right fit.

What is LiteLLM?

LiteLLM is an open source AI gateway maintained by BerriAI, a Y Combinator-backed startup. It provides a unified, OpenAI-compatible interface to over 100 LLM providers, with built-in features like cost tracking, rate limiting, and automatic fallbacks. LiteLLM also offers a Python SDK for direct client integration.

What is MLflow?

MLflow is an open source AI engineering platform for agents, LLMs, and models that enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data. With over 30 million monthly downloads, thousands of organizations rely on MLflow each day to ship AI to production with confidence.

Quick Comparison

Open Source

LiteLLM is open source under the MIT license, maintained by BerriAI, a Y Combinator-backed startup. While the core SDK is freely available, enterprise features such as SSO, audit logging, and advanced admin controls are only available for paid customers with enterprise contract. Part of the source code is also under a separate license.

MLflow is open source under Apache 2.0 and is backed by the Linux Foundation, the premier open source software foundation who also owns Linux, Kubernetes, and Pytorch. MLflow has been powering production AI since 2018 and maintains full feature parity between its open source release and managed offerings. With over 30 million monthly downloads and thousands of enterprise users, MLflow is one of the most widely deployed AI platforms.

Security & Reliability

For teams deploying AI in production, the security and reliability of the tools in their stack are not optional. When an AI gateway sits between your applications and LLM providers, it becomes a critical piece of infrastructure that handles API keys, model access, and sensitive data.

In March 2026, LiteLLM experienced a supply chain incident where compromised packages were briefly published to PyPI. BerriAI responded promptly and engaged Mandiant for forensic analysis. While such incidents can affect any open source project, this highlights the importance of evaluating the security practices and governance model of tools in your AI infrastructure.

MLflow benefits from dedicated security team from Databricks and nearly a decade of hardening for enterprise deployments. With thousands of enterprise users worldwide, MLflow has a proven track record of reliability and security at scale. The Linux Foundation governance provides additional assurance that security practices meet enterprise standards, and the large contributor community means more eyes on the code and faster identification of potential issues.

AI Gateway Capabilities

Both MLflow and LiteLLM offer AI Gateway capabilities for routing requests to multiple LLM providers, managing costs, and enforcing usage policies. Most major providers (OpenAI, Anthropic, Google, Azure, AWS Bedrock, and more) are supported by both gateways. This is the primary overlap between the two tools.

LiteLLM is purpose-built as a gateway proxy, offering broad provider support (100+), virtual key management, and an OpenAI-compatible API format that enables applications to switch providers without code changes. It offers rate limiting, cost tracking, and automatic fallbacks. However, LiteLLM operates in isolation from the rest of the AI development stack. To gain observability into gateway traffic, teams must configure external callback handlers. To evaluate model quality or optimize prompts, they need entirely separate tools.

MLflow offers a built-in AI Gateway with similar routing, rate limiting, cost tracking, and fallback capabilities, but with a critical advantage: native integration with tracing, evaluation, and prompt management. When requests flow through MLflow's gateway, they are automatically captured in traces, enabling teams to connect cost and usage data with evaluation results and prompt performance, all in a single platform.

Performance

For production AI applications, gateway performance directly impacts user experience. Every millisecond of overhead added by the gateway is multiplied across millions of requests.

We benchmarked both gateways with a 50 ms simulated provider delay (4 workers, 50 concurrent users). The overhead column shows gateway latency after subtracting the simulated delay. The results show that MLflow's AI Gateway adds approximately half the overhead and delivers 67% higher throughput compared to LiteLLM.

Observability & Monitoring

Tracing is essential for understanding how AI applications behave in production. For gateway deployments, teams need visibility into every request flowing through the system, including latency, token usage, costs, and errors.

LiteLLM does not include built-in tracing. To gain observability into gateway traffic, teams must configure an external tool such as Datadog or Langfuse as a callback handler. This means operating and maintaining a separate observability system alongside the gateway. LiteLLM also has no support for distributed tracing, so client-side agent traces and proxy-side request logs remain disconnected, making end-to-end debugging difficult.

MLflow's AI Gateway provides native tracing out of the box. When usage tracking is enabled on an endpoint, every gateway request is automatically logged as a trace with full detail: latency, token consumption, cost, and errors, with no code changes required. MLflow also supports distributed tracing via the W3C TraceContext standard, which allows client-side agent traces to automatically link to gateway traces. This gives teams end-to-end visibility from agent orchestration through the gateway down to the LLM provider, all in a single trace view.

Beyond the gateway, MLflow provides a one-line autolog() API for 30+ frameworks, including OpenAI, LangGraph, DSPy, Anthropic, LangChain, Pydantic AI, CrewAI, and many more. Traces capture full span-level detail across the entire application stack and can be viewed, searched, and analyzed directly in the MLflow UI.

Gateway TracingAgent Tracing

MLflow

# Enable "Usage Tracking" on the endpoint
# in the MLflow AI Gateway UI. Done.
#
# Every gateway request is automatically
# traced with latency, token usage, cost,
# and error details. No code changes.
from openai import OpenAI

client = OpenAI(base_url="http://localhost:5000/gateway/v1")

# Invoke the client as usual

LiteLLM

import litellm

# Observability requires configuring an
# external callback (e.g. Datadog, Langfuse).

litellm.success_callback = ["datadog"]
litellm.failure_callback = ["datadog"]

# Also requires:
# - Datadog agent running
# - DD_API_KEY configured
# - Separate dashboard setup

Beyond a Gateway: Complete AI Platform

A gateway is only one piece of a production AI stack. Teams also need observability, evaluation, prompt management, and model governance. LiteLLM does not offer any of these capabilities. Teams using LiteLLM must adopt and integrate separate tools for each of these needs, creating operational complexity and tool sprawl.

MLflow is a complete AI engineering platform that covers the entire lifecycle:

• Observability with one-line autolog() for 30+ frameworks and built-in distributed tracing across client and gateway
• Agent evaluation with built-in scorers, integration with leading evaluation libraries (RAGAS, DeepEval, Phoenix, TruLens, Guardrails AI), multi-turn evaluation, online evaluation, and LLM judge alignment with human feedback
• Prompt engineering with a versioned prompt registry, lineage tracking, and automatic optimization that improves prompts based on evaluation results using the state-of-the-art algorithms like GEPA.
• Fine-tuning and reinforcement learning support with Transformers, PEFT, Unsloth, and TRL

Summary

Choose LiteLLM if you need a dedicated LLM proxy for model routing, use long-tail LLM providers that only LiteLLM supports, or are already integrated with LiteLLM's client SDK and proxy in your stack.

Choose MLflow if you need a secure AI gateway trusted by enterprises, with native observability and evaluation integrated with your gateway. MLflow covers the entire AI lifecycle: gateway, tracing, evaluation, prompt optimization, all in one platform. Teams who build production-grade LLM applications and agents should choose MLflow.

Related Resources

• What is AI Gateway?
• What is AI Platform?
• MLflow AI Gateway Guide
• MLflow Tracing Quickstart
• MLflow Documentation
• LiteLLM Documentation
• LLMOps Guide